Identifying molecular genetic features and oncogenic pathways of clear cell renal cell carcinoma through the anatomical (PADUA) scoring system

Abstract Renal cell carcinoma (RCC) is among the top 10 malignant carcinomas1. Clear cell (cc)RCC, accounting for ~ 75% of RCC cases, is an aggressive histological RCC subtype. In the last decade, large-scale multiomics studies have profoundly enhanced our understanding of this disease2,3. However, despite the differences of genomic alterations between Western and Eastern ccRCC4,5, these studies mostly focused on patients in Western populations. Here we conducted a comprehensive proteogenomic analysis of 232 tumor and adjacent non-tumor tissue pairs from Chinese ccRCC patients. Genomic analysis revealed unique genetic features of Chinese ccRCC and distinct mutation patterns associated with copy number alterations. Based on proteomic profiles, ccRCC showed extensive metabolic dysregulation, especially in one-carbon metabolism. We classified ccRCC into three subtypes (GP1–3), among which the most aggressive GP1 exhibited dominant immune response, metastasis, and metabolic imbalance, linking the proteomic features, genomic alterations, and clinical outcomes of ccRCC. Nicotinamide N-methyltransferase (NNMT) and NNMT mediated protein homocysteinylation were identified as a poor prognosis indicator and a drug target for GP1, respectively. We demonstrated that NNMT induces DNA-dependent protein kinase catalytic subunit (DNA-PKcs) homocysteinylation, increases DNA repair, and promotes tumor growth in ccRCC. Treatment of N-acetyl-cysteine (NAC), an inhibitor of homocysteinylation, markedly reduced the NNMT overexpression induced radioresistance of tumor cells. This study provided valuable insights into the biological underpinnings and prognosis assessment of ccRCC, revealing a targetable metabolic vulnerability.

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A Non-Invasive Scoring System to Differential Diagnosis of Clear Cell Renal Cell Carcinoma (ccRCC) From Renal Angiomyolipoma Without Visible Fat (RAML-wvf) Based on CT Features

Frontiers in Oncology ◽

10.3389/fonc.2021.633034 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiao-Jie Wang ◽

Bai-Qiang Qu ◽

Jia-Ping Zhou ◽

Qiao-Mei Zhou ◽

Yuan-Fei Lu ◽

...

Keyword(s):

Computed Tomography ◽

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Scoring System ◽

Clear Cell ◽

Renal Angiomyolipoma ◽

Cell Renal Cell Carcinoma ◽

Number Of Patients ◽

Primary Model

BackgroundRenal angiomyolipoma without visible fat (RAML-wvf) and clear cell renal cell carcinoma (ccRCC) have many overlapping features on imaging, which poses a challenge to radiologists. This study aimed to create a scoring system to distinguish ccRCC from RAML-wvf using computed tomography imaging.MethodsA total of 202 patients from 2011 to 2019 that were confirmed by pathology with ccRCC (n=123) or RAML (n=79) were retrospectively analyzed by dividing them randomly into a training cohort (n=142) and a validation cohort (n=60). A model was established using logistic regression and weighted to be a scoring system. ROC, AUC, cut-off point, and calibration analyses were performed. The scoring system was divided into three ranges for convenience in clinical evaluations, and the diagnostic probability of ccRCC was calculated.ResultsFour independent risk factors are included in the system: 1) presence of a pseudocapsule, 2) a heterogeneous tumor parenchyma in pre-enhancement scanning, 3) a non-high CT attenuation in pre-enhancement scanning, and 4) a heterogeneous enhancement in CMP. The prediction accuracy had an ROC of 0.978 (95% CI, 0.956–0.999; P=0.011), similar to the primary model (ROC, 0.977; 95% CI, 0.954–1.000; P=0.012). A sensitivity of 91.4% and a specificity of 93.9% were achieved using 4.5 points as the cutoff value. Validation showed a good result (ROC, 0.922; 95% CI, 0.854–0.991, P=0.035). The number of patients with ccRCC in the three ranges (0 to <2 points; 2–4 points; >4 to ≤11 points) significantly increased with increasing scores.ConclusionThis scoring system is convenient for distinguishing between ccRCC and RAML-wvf using four computed tomography features.

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